C# OpenVINO Nail Seg 指甲分割指甲检测

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模型信息

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数据集

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C# OpenVINO Nail Seg 指甲分割指甲检测

效果

模型信息

Model Properties

date：2024-02-29T16:41:28.273760

author：Ultralytics

task：segment

version：8.1.18

stride：32

batch：1

imgsz：[640, 640]

names：{0: 'Nail'}

Inputs

name：images

tensor：Float[1, 3, 640, 640]

Outputs

name：output0

tensor：Float[1, 37, 8400]

name：output1

tensor：Float[1, 32, 160, 160]

项目

代码

using OpenCvSharp;

using Sdcb.OpenVINO;

using Sdcb.OpenVINO.Natives;

using System;

using System.Collections.Generic;

using System.Diagnostics;

using System.Drawing;

using System.IO;

using System.Text;

using System.Windows.Forms;

namespace OpenVINO_Seg

{

public partial class Form1 : Form

{

public Form1()

{

InitializeComponent();

}

string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";

string image_path = "";

string startupPath;

string model_path;

string classer_path;

Mat src;

SegmentationResult result_pro;

Mat result_image;

Result seg_result;

StringBuilder sb = new StringBuilder();

float[] det_result_array = new float[8400 * 37];

float[] proto_result_array = new float[32 * 160 * 160];

// 识别结果类型

public string[] class_names;

private void button1_Click(object sender, EventArgs e)

{

OpenFileDialog ofd = new OpenFileDialog();

ofd.Filter = fileFilter;

if (ofd.ShowDialog() != DialogResult.OK) return;

pictureBox1.Image = null;

image_path = ofd.FileName;

pictureBox1.Image = new Bitmap(image_path);

textBox1.Text = "";

src = new Mat(image_path);

pictureBox2.Image = null;

}

unsafe private void button2_Click(object sender, EventArgs e)

{

if (pictureBox1.Image == null)

{

return;

}

pictureBox2.Image = null;

textBox1.Text = "";

sb.Clear();

src = new Mat(image_path);

Model rawModel = OVCore.Shared.ReadModel(model_path);

PrePostProcessor pp = rawModel.CreatePrePostProcessor();

PreProcessInputInfo inputInfo = pp.Inputs.Primary;

inputInfo.TensorInfo.Layout = Sdcb.OpenVINO.Layout.NHWC;

inputInfo.ModelInfo.Layout = Sdcb.OpenVINO.Layout.NCHW;

Model m = pp.BuildModel();

CompiledModel cm = OVCore.Shared.CompileModel(m, "CPU");

InferRequest ir = cm.CreateInferRequest();

Shape inputShape = m.Inputs[0].Shape;

float[] factors = new float[4];

factors[0] = 1f * src.Width / inputShape[2];

factors[1] = 1f * src.Height / inputShape[1];

factors[2] = src.Rows;

factors[3] = src.Cols;

result_pro = new SegmentationResult(class_names, factors,0.3f,0.5f);

Stopwatch stopwatch = new Stopwatch();

Mat resized = src.Resize(new OpenCvSharp.Size(inputShape[2], inputShape[1]));

Mat f32 = new Mat();

resized.ConvertTo(f32, MatType.CV_32FC3, 1.0 / 255);

using (Tensor input = Tensor.FromRaw(

new ReadOnlySpan<byte>((void*)f32.Data, (int)((long)f32.DataEnd - (long)f32.DataStart)),

new Shape(1, f32.Rows, f32.Cols, 3),

ov_element_type_e.F32))

{

ir.Inputs.Primary = input;

}

double preprocessTime = stopwatch.Elapsed.TotalMilliseconds;

stopwatch.Restart();

ir.Run();

double inferTime = stopwatch.Elapsed.TotalMilliseconds;

stopwatch.Restart();

using (Tensor output_det = ir.Outputs[0])

using (Tensor output_proto = ir.Outputs[1])

{

det_result_array = output_det.GetData<float>().ToArray();

proto_result_array = output_proto.GetData<float>().ToArray();

seg_result = result_pro.process_result(det_result_array, proto_result_array);

double postprocessTime = stopwatch.Elapsed.TotalMilliseconds;

stopwatch.Stop();

double totalTime = preprocessTime + inferTime + postprocessTime;

result_image = src.Clone();

Mat masked_img = new Mat();

// 将识别结果绘制到图片上

for (int i = 0; i < seg_result.length; i++)

{

Cv2.Rectangle(result_image, seg_result.rects[i], new Scalar(0, 0, 255), 2, LineTypes.Link8);

Cv2.Rectangle(result_image, new OpenCvSharp.Point(seg_result.rects[i].TopLeft.X, seg_result.rects[i].TopLeft.Y - 20),

new OpenCvSharp.Point(seg_result.rects[i].BottomRight.X, seg_result.rects[i].TopLeft.Y), new Scalar(0, 255, 255), -1);

Cv2.PutText(result_image, seg_result.classes[i] + "-" + seg_result.scores[i].ToString("0.00"),

new OpenCvSharp.Point(seg_result.rects[i].X, seg_result.rects[i].Y - 5),

HersheyFonts.HersheySimplex, 0.6, new Scalar(0, 0, 0), 1);

Cv2.AddWeighted(result_image, 0.5, seg_result.masks[i], 0.5, 0, masked_img);

sb.AppendLine($"{seg_result.classes[i]}:{seg_result.scores[i]:P0}");

}

if (seg_result.length > 0)

{

if (pictureBox2.Image != null)

{

pictureBox2.Image.Dispose();

}

pictureBox2.Image = new Bitmap(masked_img.ToMemoryStream());

sb.AppendLine($"Preprocess: {preprocessTime:F2}ms");

sb.AppendLine($"Infer: {inferTime:F2}ms");

sb.AppendLine($"Postprocess: {postprocessTime:F2}ms");

sb.AppendLine($"Total: {totalTime:F2}ms");

textBox1.Text = sb.ToString();

}

else

{

textBox1.Text = "无信息";

}

masked_img.Dispose();

result_image.Dispose();

}

private void Form1_Load(object sender, EventArgs e)

{

image_path = "1.jpg";

pictureBox1.Image = new Bitmap(image_path);

startupPath = Application.StartupPath;

model_path = startupPath + "\\nails_seg_s_yolov8.onnx";

classer_path = startupPath + "\\lable.txt";

List<string> str = new List<string>();

StreamReader sr = new StreamReader(classer_path);

string line;

while ((line = sr.ReadLine()) != null)

{

str.Add(line);

}

class_names = str.ToArray();

}

复制代码

using OpenCvSharp;
using Sdcb.OpenVINO;
using Sdcb.OpenVINO.Natives;
using System;
using System.Collections.Generic;
using System.Diagnostics;
using System.Drawing;
using System.IO;
using System.Text;
using System.Windows.Forms;

namespace OpenVINO_Seg
{
    public partial class Form1 : Form
    {
        public Form1()
        {
            InitializeComponent();
        }

        string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";
        string image_path = "";
        string startupPath;
        string model_path;
        string classer_path;

        Mat src;

        SegmentationResult result_pro;
        Mat result_image;
        Result seg_result;

        StringBuilder sb = new StringBuilder();

        float[] det_result_array = new float[8400 * 37];
        float[] proto_result_array = new float[32 * 160 * 160];

        // 识别结果类型
        public string[] class_names;

        private void button1_Click(object sender, EventArgs e)
        {
            OpenFileDialog ofd = new OpenFileDialog();
            ofd.Filter = fileFilter;
            if (ofd.ShowDialog() != DialogResult.OK) return;
            pictureBox1.Image = null;
            image_path = ofd.FileName;
            pictureBox1.Image = new Bitmap(image_path);
            textBox1.Text = "";
            src = new Mat(image_path);
            pictureBox2.Image = null;
        }

        unsafe private void button2_Click(object sender, EventArgs e)
        {
            if (pictureBox1.Image == null)
            {
                return;
            }

            pictureBox2.Image = null;
            textBox1.Text = "";
            sb.Clear();

            src = new Mat(image_path);

            Model rawModel = OVCore.Shared.ReadModel(model_path);
            PrePostProcessor pp = rawModel.CreatePrePostProcessor();
            PreProcessInputInfo inputInfo = pp.Inputs.Primary;

            inputInfo.TensorInfo.Layout = Sdcb.OpenVINO.Layout.NHWC;
            inputInfo.ModelInfo.Layout = Sdcb.OpenVINO.Layout.NCHW;

            Model m = pp.BuildModel();
            CompiledModel cm = OVCore.Shared.CompileModel(m, "CPU");
            InferRequest ir = cm.CreateInferRequest();

            Shape inputShape = m.Inputs[0].Shape;

            float[] factors = new float[4];
            factors[0] = 1f * src.Width / inputShape[2];
            factors[1] = 1f * src.Height / inputShape[1];
            factors[2] = src.Rows;
            factors[3] = src.Cols;

            result_pro = new SegmentationResult(class_names, factors,0.3f,0.5f);

            Stopwatch stopwatch = new Stopwatch();
            Mat resized = src.Resize(new OpenCvSharp.Size(inputShape[2], inputShape[1]));
            Mat f32 = new Mat();
            resized.ConvertTo(f32, MatType.CV_32FC3, 1.0 / 255);

            using (Tensor input = Tensor.FromRaw(
                 new ReadOnlySpan<byte>((void*)f32.Data, (int)((long)f32.DataEnd - (long)f32.DataStart)),
                new Shape(1, f32.Rows, f32.Cols, 3),
                ov_element_type_e.F32))
            {
                ir.Inputs.Primary = input;
            }
            double preprocessTime = stopwatch.Elapsed.TotalMilliseconds;
            stopwatch.Restart();

            ir.Run();
            double inferTime = stopwatch.Elapsed.TotalMilliseconds;
            stopwatch.Restart();

            using (Tensor output_det = ir.Outputs[0])
            using (Tensor output_proto = ir.Outputs[1])
            {
                det_result_array = output_det.GetData<float>().ToArray();
                proto_result_array = output_proto.GetData<float>().ToArray();

                seg_result = result_pro.process_result(det_result_array, proto_result_array);

                double postprocessTime = stopwatch.Elapsed.TotalMilliseconds;
                stopwatch.Stop();

                double totalTime = preprocessTime + inferTime + postprocessTime;

                result_image = src.Clone();
                Mat masked_img = new Mat();

                // 将识别结果绘制到图片上
                for (int i = 0; i < seg_result.length; i++)
                {
                    Cv2.Rectangle(result_image, seg_result.rects[i], new Scalar(0, 0, 255), 2, LineTypes.Link8);
                    Cv2.Rectangle(result_image, new OpenCvSharp.Point(seg_result.rects[i].TopLeft.X, seg_result.rects[i].TopLeft.Y - 20),
                        new OpenCvSharp.Point(seg_result.rects[i].BottomRight.X, seg_result.rects[i].TopLeft.Y), new Scalar(0, 255, 255), -1);
                    Cv2.PutText(result_image, seg_result.classes[i] + "-" + seg_result.scores[i].ToString("0.00"),
                        new OpenCvSharp.Point(seg_result.rects[i].X, seg_result.rects[i].Y - 5),
                        HersheyFonts.HersheySimplex, 0.6, new Scalar(0, 0, 0), 1);
                    Cv2.AddWeighted(result_image, 0.5, seg_result.masks[i], 0.5, 0, masked_img);

                    sb.AppendLine($"{seg_result.classes[i]}:{seg_result.scores[i]:P0}");
                }

                if (seg_result.length > 0)
                {
                    if (pictureBox2.Image != null)
                    {
                        pictureBox2.Image.Dispose();
                    }
                    pictureBox2.Image = new Bitmap(masked_img.ToMemoryStream());
                    sb.AppendLine($"Preprocess: {preprocessTime:F2}ms");
                    sb.AppendLine($"Infer: {inferTime:F2}ms");
                    sb.AppendLine($"Postprocess: {postprocessTime:F2}ms");
                    sb.AppendLine($"Total: {totalTime:F2}ms");
                    textBox1.Text = sb.ToString();
                }
                else
                {
                    textBox1.Text = "无信息";
                }

                masked_img.Dispose();
                result_image.Dispose();
            }
        }

        private void Form1_Load(object sender, EventArgs e)
        {
            image_path = "1.jpg";
            pictureBox1.Image = new Bitmap(image_path);

            startupPath = Application.StartupPath;

            model_path = startupPath + "\\nails_seg_s_yolov8.onnx";
            classer_path = startupPath + "\\lable.txt";

            List<string> str = new List<string>();
            StreamReader sr = new StreamReader(classer_path);
            string line;
            while ((line = sr.ReadLine()) != null)
            {
                str.Add(line);
            }
            class_names = str.ToArray();

        }
    }
}

数据集

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源码下载

C# OpenVINO Nail Seg 指甲分割 指甲检测

效果

模型信息

项目

代码

数据集

下载

C# OpenVINO Nail Seg 指甲分割指甲检测